As part of an effort to understand psychological consequences among family members of patients sustaining a traumatic injury, medical research has turned to the role of resilience – or the ability to bounce back from and maintain psychological well-being in the wake of an adverse event— in mitigating the potential distress (i.e., depression and burden) of caregiving (Bonanno, 2004; Roberson et al., under review). This study sought to examine the ability for trait-resilience to predict trajectories of distress over the course of a year among 124 family members and loved ones of patients admitted to a Level I Trauma Center. A cross-lagged path model examining resilience, burden, and depression at baseline, 3, 6, and 12 months after injury showed that, while depression strongly predicted later burden, resilience was not a significant predictor of either outcome in the model. When depression and burden were subjected to a person-centered analysis (i.e., latent growth curve analysis), two major classes were identified: caregivers with high, chronic distress (33% of the sample) and low-moderate distress that declined over time. A three-class solution for caregiver burden further identified a moderate, increasing trajectory class. Predictive discriminant analyses revealed that trait-resilience was a major differentiating trait between class …

Understanding temporal relations among posttraumatic stress disorder (PTSD) symptom dimensions has received increasing attention in research. However, current findings in this area are limited by group-level approaches, which are based on inter-individual variation. PTSD is a heterogeneous syndrome and symptoms are likely to vary across individuals and time. Thus, it is important to examine temporal relations among PTSD symptom dimensions as dynamic processes and at the level of intra-individual variation. The aim of the present study was to capture temporal dynamics among PTSD symptom dimensions at an individual level using unified structural equation modeling (uSEM). World Trade Center (WTC) 9/11 responders (N = 202) oversampled for current PTSD (18.3% met criteria in past month) were recruited from the Long Island site of the WTC health program. Using ecological momentary assessment (EMA), PTSD symptoms were assessed three times a day over seven consecutive days. The person-specific temporal relations among PTSD symptom dimensions were estimated with individual-level uSEM. For the sample as a whole, hyperarousal played a key role in driving the other three symptom dimensions longitudinally, with the strongest effect in intrusive symptoms. However, daily temporal relations among PTSD symptoms were idiosyncratic. Although hyperarousal was a strong predictor of subsequent symptom …

There are many identified factors that correlate with whether an individual experiences adverse symptoms following a traumatic event. Research indicates that betrayal, where the victim is betrayed by another individual, may be one of these factors. Betrayal Trauma Theory (BTT) posits that betrayal during trauma increases the likelihood of developing adverse psychological symptoms. BTT objectively dichotomizes traumas as high betrayal or low betrayal traumas based on details of the event. Though literature finds those who experience high betrayals experience more severe symptoms of PTSD, depression, anxiety, and dissociation than those who experience low betrayals, this objective classification is limited as it is based solely on the circumstances of the trauma and does not give the victim the option of indicating whether they felt betrayed. We believe perceptions of betrayal, whether the event meets the objective criteria of betrayal put forth by BTT or not, is an important predictor of trauma-related outcomes. This study (N = 244) found that perceived betrayal predicts symptoms of PTSD and depression independently, as well as when controlling for objective betrayal, dependence on the perpetrator, event centrality, anxiety, and dissociation. These findings indicate perceived betrayal is a unique construct that should be included in the operationalization …

Alcohol-related motor vehicle crashes are a major preventable cause of death in the United States. One potential factor that may modulate the influence of alcohol intoxication on driving-related decision-making is posttraumatic stress. The current study evaluated the influence of induction of acute trauma-related stress (via script-driven imagery) during alcohol intoxication (.06% BrAC) on driving-related risky decision-making – willingness to drive, driving-related decision-making (i.e., attempted red light runs), and driving-related reaction time (i.e., braking latency) – among 56 trauma-exposed (currently symptomatic) adult drinkers from the community (M = 25.32; 46.4% female). Results indicated that trauma-related stress may exacerbate willingness to drive during a state of acute alcohol intoxication, but, alternatively, may have only a minimal-to-moderate effect on performance-based, driving-related decision-making (i.e., red light runs), and a potentially mitigating impact on driving-related reaction time (i.e., braking latency) under the influence of alcohol. Generally, results suggest that trauma-related stress may differentially impact varying aspects of driving-related risky decision-making, above and beyond the influence of alcohol. Implications for theoretical modeling for driving-related decision-making during acute intoxication and for the advancement of education and intervention efforts, as well as suggestions for future directions, including methodological and procedural improvements, are discussed.

The purpose of this research was two-fold: (1) further validate the Virtual Reality Stroop Task HMMWV [VRST; Stroop stimuli embedded within a virtual high mobility multipurpose wheeled vehicle] via a comparison of the 3-dimensional VRST factor structure to that of a 2-dimensional computerized version of the Stroop task; and (2) model the performance of machine learning [ML] classifiers and hyper-parameters for an adaptive version of the VRST. Both the 3-D VRST and 2-D computerized Stroop tasks produced two-factor solutions: an accuracy factor and a reaction time factor. The factors had low correlations suggesting participants may be focusing on either responding to stimuli accurately or swiftly. In future studies researchers may want to consider throughput, a measure of correct responses per unit of time. The assessment of naive Bayes (NB), k-nearest neighbors (kNN), and support vector machines (SVM) machine learning classifiers found that SVM classifiers tended to have the highest accuracies and greatest areas under the curve when classifying users as high or low performers. NB also performed well but kNN algorithms did not. As such, SVM and NB may be the best candidates for creation of an adaptive version of the VRST.